The present disclosure generally relates to data learning and big data analytics. More particularly, and without limitation, the present disclosure relates to systems and methods for data analysis and identity management through machine learning, hierarchical learning, or structured learning.
Advances in data analytics have been driven by improvements in mobile and cloud computing, sensor technology, and computer vision. Such advances may be leveraged in a number of different ways, and may be used to provide better automated decision making in numerous different contexts. For example, consumers may be provided with improved service and retail experiences, patients at hospitals may be given improved treatment, and public utilities may be operated with greater efficiency.
However, challenges may arise when collecting and storing large amounts of electronic data. One challenge involves the scalability of data analytics systems. For example, an entity that has deployed a data analytics system at one location and wants to deploy additional data analytics systems at other locations may run into issues with integrating the multiple deployed systems and the data they collect.
Another challenge relates to managing the collected data, quickly analyzing it to gain an understanding of its importance, and using it in a meaningful way. For example, conventional data analytics systems may collect large amounts of data for a retail property owner and/or a retailer pertaining to the behavior and activity of their customers (and potential customers) but may not be able to quickly synthesize the collected data so that effective and timely decisions can be made. Moreover, the retailer and/or property owner may not understand how all the collected data fits together to form a context of a customer's preferences, habits, and behavior.